synthetic datasets
Datasets generated artificially rather than collected from real-world observations, often used for training and testing AI models in scenarios where real data is scarce.
- $\text{G}^2\text{M}$: A Generalized Gaussian Mirror Method to Boost Feature Selection Power
- A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models
- Ambient Proteins - Training Diffusion Models on Noisy Structures
- An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks
- Complete Structure Guided Point Cloud Completion via Cluster- and Instance-Level Contrastive Learning
- Conformal Prediction for Causal Effects of Continuous Treatments
- Conformal Prediction for Time-series Forecasting with Change Points
- Convolution Goes Higher-Order: A Biologically Inspired Mechanism Empowers Image Classification
- Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization
- Dense Metric Depth Estimation via Event-based Differential Focus Volume Prompting
- Distributionally Robust Feature Selection
- Extracting task-relevant preserved dynamics from contrastive aligned neural recordings
- FACT: Mitigating Inconsistent Hallucinations in LLMs via Fact-Driven Alternating Code-Text Training
- FairDD: Fair Dataset Distillation
- Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
- FraPPE: Fast and Efficient Preference-Based Pure Exploration
- Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
- Generative Distribution Embeddings
- HouseLayout3D: A Benchmark and Training-free Baseline for 3D Layout Estimation in the Wild
- How Classifier Features Transfer to Downstream: An Asymptotic Analysis in a Two-Layer Model
- Incentivizing Time-Aware Fairness in Data Sharing
- Individually Fair Diversity Maximization
- Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models
- ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
- Joint Velocity-Growth Flow Matching for Single-Cell Dynamics Modeling
- LLM Interpretability with Identifiable Temporal-Instantaneous Representation
- Learning Across the Gap: Hybrid Multi-armed Bandits with Heterogeneous Offline and Online Data
- Learning Provably Improves the Convergence of Gradient Descent
- Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
- Learning-Augmented Streaming Algorithms for Correlation Clustering
- Merlin L48 Spectrogram Dataset
- Missing Data Imputation by Reducing Mutual Information with Rectified Flows
- Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
- Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables
- Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion
- OrdShap: Feature Position Importance for Sequential Black-Box Models
- Over-squashing in Spatiotemporal Graph Neural Networks
- Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model
- PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
- Prediction-Powered Semi-Supervised Learning with Online Power Tuning
- Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations
- ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
- Spectral Analysis of Representational Similarity with Limited Neurons
- SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
- Taming generative video models for zero-shot optical flow extraction
- Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
- Topology-Aware Conformal Prediction for Stream Networks
- TreeSplat: Mergeable Tree for Deformable Gaussian Splatting
- Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis
- Unlocker: Disentangle the Deadlock of Learning between Label-noisy and Long-tailed Data
- Vicinal Label Supervision for Reliable Aleatoric and Epistemic Uncertainty Estimation
- When Does Curriculum Learning Help? A Theoretical Perspective
- Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
- Why Diffusion Models Don’t Memorize: The Role of Implicit Dynamical Regularization in Training
- ZEUS: Zero-shot Embeddings for Unsupervised Separation of Tabular Data